Liu Gang, Neelamegham Sriram
Department of Chemical and Biological Engineering, and The NY State Center for Excellence in Bioinformatics and Life Sciences, State University of New York, Buffalo, New York, United States of America.
PLoS One. 2014 Jun 30;9(6):e100939. doi: 10.1371/journal.pone.0100939. eCollection 2014.
Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All the implemented features are provided as part of the Glycosylation Network Analysis Toolbox (GNAT), an open-source, platform-independent, MATLAB based toolbox for studies of Systems Glycobiology.
糖基化是迄今为止发现的最常见、最复杂的翻译后修饰之一。它通过多个酶家族的催化作用进行,这些酶家族包括将单糖添加到不断增长的聚糖上的糖基转移酶,以及去除糖残基以修剪聚糖的糖苷酶。这些酶的表达水平和特异性部分地调节特定细胞/组织系统的聚糖分布或糖组。目前,尚无系统的方法来描述催化糖基化的酶和细胞反应网络。为了解决这一局限性,我们提出了一种简化的、机器可读的糖基化酶定义以及构建和分析糖基化反应网络的其他方法。在这个计算框架中,酶类经过系统设计,用于存储详细的特异性数据,如酶功能基团、连接和底物特异性。当给定反应物和/或产物列表以及系统中存在的酶时,新的类别及其相关功能能够进行单反应推断和自动全网络重建。此外,图论用于支持映射网络中两个或多个物种之间连通性的功能,并生成子集模型以识别调节聚糖生物合成的限速步骤。最后,这个框架允许使用质谱(MS)数据合成生化反应网络。通过三个案例研究说明了上述特征,这些案例研究包括:i)功能性选择素配体构建过程中的O-连接聚糖生物合成;ii)自动N-连接糖基化途径构建;iii)基于糖组学的MS数据的处理和分析。总体而言,新的计算框架通过整合聚糖结构和酶生物化学知识,实现了糖基化网络模型的自动构建和分析。所有实现的功能都作为糖基化网络分析工具箱(GNAT)的一部分提供,GNAT是一个基于MATLAB的开源、平台独立的工具箱,用于系统糖生物学研究。